DR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c7d70a7",
   "metadata": {},
   "source": [
    "# HANDWRITTEN DIGIT RECOGNITION\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c167e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyscreenshot as ImageGrab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d09853df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "\n",
    "import time\n",
    "images_folder=\"Capture_Images/5/\"\n",
    "\n",
    "for i in range(0,100):\n",
    "   time.sleep(8)\n",
    "   im=ImageGrab.grab(bbox=(60,170,400,550)) #x1,y1,x2,y2\n",
    "   print(\"saved......\",i)\n",
    "   im.save(images_folder+str(i)+'.png')\n",
    "   print(\"clear screen now and redraw now........\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b79023d",
   "metadata": {},
   "source": [
    "# GENERATE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "798d76e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import csv\n",
    "import glob\n",
    "\n",
    "header=[\"label\"]\n",
    "\n",
    "for i in range(0,784):\n",
    "    header.append(\"pixel\"+str(i))\n",
    "with open('dataset.csv','a') as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow(header)\n",
    "    \n",
    "for label in range(10):\n",
    "    dirList = glob.glob(\"Capture_Images/\"+str(label)+\"/*.png\")\n",
    "    \n",
    "    for img_path in dirList:\n",
    "        im= cv2.imread(img_path)\n",
    "        im_gray= cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n",
    "        im_gray = cv2.GaussianBlur(im_gray,(15,15), 0)\n",
    "        roi= cv2.resize(im_gray,(28,28),interpolation=cv2.INTER_AREA)\n",
    "        \n",
    "        data=[]\n",
    "        data.append(label)\n",
    "        rows,cols= roi.shape\n",
    "        \n",
    "        for i in range(rows):\n",
    "            for j in range(cols):\n",
    "                k=roi[i,j]\n",
    "                if k>100:\n",
    "                    k=1\n",
    "                else:\n",
    "                    k=0\n",
    "                data.append(k)\n",
    "        with open('dataset.csv','a') as f:\n",
    "            writer=csv.writer(f)\n",
    "            writer.writerow(data)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c744521c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c772fc",
   "metadata": {},
   "source": [
    "# LOAD DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e810108",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "370      3       0       0       0       0       0       0       0       0   \n",
       "558      5       0       0       0       0       0       0       0       0   \n",
       "638      6       0       0       0       0       0       0       0       0   \n",
       "66       0       0       0       0       0       0       0       0       0   \n",
       "960      9       0       0       0       0       0       0       0       0   \n",
       "..     ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "945      9       0       0       0       0       0       0       0       0   \n",
       "167      1       0       0       0       0       0       0       0       0   \n",
       "383      3       0       0       0       0       0       0       0       0   \n",
       "705      7       0       0       0       0       0       0       0       0   \n",
       "457      4       0       0       0       0       0       0       0       0   \n",
       "\n",
       "     pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "370       0  ...         0         0         0         0         0         0   \n",
       "558       0  ...         0         0         0         0         0         0   \n",
       "638       0  ...         1         1         1         0         0         0   \n",
       "66        0  ...         0         0         0         0         0         0   \n",
       "960       0  ...         0         0         0         0         0         0   \n",
       "..      ...  ...       ...       ...       ...       ...       ...       ...   \n",
       "945       0  ...         0         0         0         0         0         0   \n",
       "167       0  ...         0         0         0         0         0         0   \n",
       "383       0  ...         0         0         0         0         0         0   \n",
       "705       0  ...         0         0         0         0         0         0   \n",
       "457       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "     pixel780  pixel781  pixel782  pixel783  \n",
       "370         0         0         0         0  \n",
       "558         0         0         0         0  \n",
       "638         0         0         0         0  \n",
       "66          0         0         0         0  \n",
       "960         0         0         0         0  \n",
       "..        ...       ...       ...       ...  \n",
       "945         0         0         0         0  \n",
       "167         0         0         0         0  \n",
       "383         0         0         0         0  \n",
       "705         0         0         0         0  \n",
       "457         0         0         0         0  \n",
       "\n",
       "[1000 rows x 785 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "data  =pd.read_csv('dataset.csv')\n",
    "data=shuffle(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c29cc5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#separation of dependent and independent variable\n",
    "X = data.drop([\"label\"],axis=1)\n",
    "Y= data[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c902a3e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c9b249ca30>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#preview of one image using matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "idx = 90\n",
    "img = X.loc[idx].values.reshape(28,28)\n",
    "print(Y[idx])\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0909e7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train-Test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_x,test_x,train_y,test_y = train_test_split(X,Y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "699a0bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model/digit_recognizer']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the model using svc and also to save the model using joblib\n",
    "import joblib\n",
    "from sklearn.svm import SVC\n",
    "classifier=SVC(kernel=\"linear\", random_state=6)\n",
    "classifier.fit(train_x.values,train_y.values)\n",
    "joblib.dump(classifier, \"model/digit_recognizer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5a037412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy=  0.97\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but SVC was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#calculate accuracy\n",
    "from sklearn import metrics\n",
    "prediction=classifier.predict(test_x)\n",
    "print(\"Accuracy= \",metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0bbaad8f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 6\n",
      "Prediction: 6\n",
      "Prediction: 6\n",
      "Prediction: 6\n",
      "Prediction: 6\n",
      "Prediction: 6\n",
      "Prediction: 6\n",
      "Prediction: 7\n",
      "Prediction: 7\n",
      "Prediction: 5\n",
      "Prediction: 5\n",
      "Prediction: 7\n",
      "Prediction: 7\n",
      "Prediction: 7\n",
      "Prediction: 7\n",
      "Prediction: 6\n",
      "Prediction: 4\n",
      "Prediction: 4\n",
      "Prediction: 7\n",
      "Prediction: 7\n",
      "Prediction: 7\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [10]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     38\u001b[0m cv2\u001b[38;5;241m.\u001b[39mnamedWindow(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     39\u001b[0m cv2\u001b[38;5;241m.\u001b[39mimshow(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult\u001b[39m\u001b[38;5;124m\"\u001b[39m,im)\n\u001b[1;32m---> 40\u001b[0m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitKey\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cv2\u001b[38;5;241m.\u001b[39mwaitKey(\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m13\u001b[39m:\n\u001b[0;32m     42\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "import cv2\n",
    "import numpy as np\n",
    "import time\n",
    "import pyscreenshot as ImageGrab\n",
    "\n",
    "model= joblib.load(\"model/digit_recognizer\")\n",
    "images_folder=\"img/\"\n",
    "\n",
    "while True:\n",
    "    img=ImageGrab.grab(bbox=(60,170,400,500))\n",
    "   \n",
    "    img.save(images_folder+\"img.png\")\n",
    "    im = cv2.imread(images_folder+\"img.png\")\n",
    "    im_gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n",
    "    im_gray  =cv2.GaussianBlur(im_gray, (15,15), 0)\n",
    "\n",
    "    #Threshold the image\n",
    "    ret, im_th = cv2.threshold(im_gray,100, 255, cv2.THRESH_BINARY)\n",
    "    roi = cv2.resize(im_th, (28,28), interpolation  =cv2.INTER_AREA)\n",
    "\n",
    "    rows,cols=roi.shape\n",
    "\n",
    "    X = []\n",
    "    \n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            k=roi[i,j]\n",
    "            if k>100:\n",
    "                k=1\n",
    "            else:\n",
    "                k=0\n",
    "            X.append(k)\n",
    "    predictions=model.predict([X])\n",
    "    print(\"Prediction:\",predictions[0])\n",
    "    cv2.putText(im,\"Prediction is: \"+str(predictions[0]),(20,20),0,0.8,(0,255,0),2,cv2.LINE_AA)\n",
    "    cv2.startWindowThread()\n",
    "    cv2.namedWindow(\"Result\")\n",
    "    cv2.imshow(\"Result\",im)\n",
    "    cv2.waitKey(1000)\n",
    "    if cv2.waitKey(1)==13:\n",
    "        break\n",
    "        \n",
    "cv2.destroyAllWindows()\n",
    "        \n"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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